Does Chronic Kidney Disease Influence Revascularization Strategy After Acute Coronary Syndrome? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Coronary artery bypass grafting (CABG) provides superior long-term outcomes to percutaneous coronary intervention (PCI) for complex multivessel coronary artery disease (CAD). People with chronic kidney disease (CKD) have increased prevalence of multivessel CAD, but also increased surgical risk. We investigated whether CKD predicted real-world use of CABG, versus PCI, in patients revascularized for acute coronary syndrome (ACS). Methods: Embase, MEDLINE, Scopus and CENTRAL were searched to identify articles referring to ACS and invasive coronary intervention in high-income countries (2012 - 2023). Articles were included if CABG rates were reported in ACS patients with and without CKD receiving revascularization. CKD was defined as an estimated glomerular filtration rate < 60 mL/min/1.73 m2; proxy definitions were accepted. Random effect meta-analyses were used to determine the average effect of CKD on odds of CABG, stratified by ACS type and dialysis use. Results: Searches generated 15,138 articles, of which 13 observational studies were included (n = 1,682,207). Amongst revascularized ACS patients, those with CKD were more likely to receive CABG than those without (pooled odds ratio (OR) = 1.50 (95% confidence interval (CI) = 1.30 - 1.72). This association was stronger following ST-elevation myocardial infarction (STEMI) than non-ST-elevation ACS (NSTE-ACS) (OR: 1.54 (95% CI: 1.23 - 1.93)) versus 1.16 (1.10 - 1.23), respectively). Conclusions: In high-income countries, revascularized ACS patients with CKD receive CABG (versus PCI) more frequently than those without kidney disease. However, accounting for lower use of coronary angiography in the CKD population removed this association following NSTE-ACS. Greater use of invasive angiography in those with NSTE-ACS and CKD might therefore increase access to revascularization, and thereby improve outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".